How to Analyze Regression Results in Excel

Running a regression analysis in Excel is a powerful way to uncover relationships hidden in your data, but that "Summary" output table can feel like reading a foreign language. This article will translate it into plain English. We'll go line by line to explain what each key metric signifies so you can confidently interpret your results and make smarter decisions for your business.

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Before You Begin: Activating the Data Analysis ToolPak

First things first, to run a regression in Excel, you’ll need the Data Analysis ToolPak enabled. This free add-in unlocks several advanced statistical tools that Excel doesn’t provide by default. Here’s a quick guide to install it if you haven’t already:

On Windows:

1. Go to File > Options.
2. In the pop-up, click on Add-ins on the left navigation.
3. At the bottom of the options, where it says "Manage," make sure Excel Add-ins is selected and click Go...
4. Check the box for Analysis ToolPak and click OK.

On Mac:

1. Go to the Tools dropdown in the menu bar at the top of your screen.
2. Click on Excel Add-ins...
3. Verify the box for Analysis ToolPak and click OK.

When you go to the Data tab on the Excel Ribbon, you should now see a Data Analysis button on the far right. You are now ready to get started.

How to Run a Regression Analysis in Excel

To keep things practical, let's use a simple business example. Imagine you want to understand what influences your e-commerce sales. You suspect that sales are influenced by a few factors: how much you spend on Facebook Ads, how much you spend on Google Ads, and how many email promotions you send out each month.

• Your Dependent Variable (or "Y") is what you want to predict: in this case, Monthly Sales.
• Your Independent Variables (or "X" variables) are the factors that you think might predict your dependent variable: Facebook Ad Spending, Google Ad Spending, and Monthly Marketing Emails Sent.

In your spreadsheet, you structure your data into columns for each of these variables. Then, go to Data > Data Analysis, select Regression from the list, and fill out the fields:

• Input Y Range: Select the entire range of your dependent variable (e.g., the cells containing your monthly sales data, including the header label).
• Input X Range: Select the entire range of your independent variables (e.g., the cells for Facebook Ads, Google Ads, and emails sent combined, including headers).
• Make sure to select the Labels option if you included the headers in your range selections, which is crucial for readability.
• Click OK. Excel places the output table on a new worksheet. Now comes the important part: interpreting the results.

Breaking Down the Output: What the Numbers Mean

Excel displays the regression results in a set of tables: one Summary Output table and an ANOVA table at the top, followed by the coefficients table at the bottom. Each table answers a key part of how strong your model is and the role that each part plays. Instead of tackling all the figures thrown at you, you can find most of what you need in just a handful of key metrics.

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Section 1: How Well Does Your Model Fit? - The Summary Output Table

The top “Summary Output” table gives you a high-level view of how well your chosen independent variables explain your dependent variable. It answers the simple question of: "Collectively, how accurately do your inputs predict your results?"

There are two figures that provide most of the answer right off the bat.

1: R Squared (or Adjusted R Squared)

This is one of the most important numbers in that chart. Expressed as a value between 0 and 1, the higher the R Squared value, the better your model fits the data.

• R Squared tells you the proportion of variance in your dependent variable (e.g., Monthly Sales) that can be explained by your independent variables (e.g., Facebook Ad Spend). An R Squared of .75 means that your independent variables explain 75% of the variation in sales data. Not bad at all!
• Adjusted R Squared is often more valuable because it accounts for the number of independent variables in your model. Each time you add a new "X" variable (e.g., Facebook Ads, Google Ads), your R Squared will only increase, even if you add pure noise. The Adjusted R Squared penalizes you for adding variables that don't really improve the model. Therefore, when choosing between two different regression models, the one with the higher Adjusted R Squared value is generally considered the best.

2: Significance F: Is Your Result Statistically Significant?

This number answers a critical pass/fail question: "Is there a risk that my findings are just due to chance?" The Significance F value is essentially a p-value for your entire model.

• If this value is very small (typically less than a predefined threshold like 0.05), you can be confident your model is statistically significant. That means your independent variables collectively have a real relationship with your dependent variable, the results are likely not just random noise.
• If the value is larger than 0.05, then your model is not statistically significant, and you probably shouldn't put too much trust in its predictions until you explore other variables. The whole point of a regression is to find out which variables are making an impact, and the Significance F value indicates whether any of them are significant.

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Section 2: Which Variables Matter? - The Coefficients Table

The bottom table contains the most useful, actionable information. It tells you about the individual relationships between each of your independent variables and your dependent variable. Here you can see which factors matter, in what direction they cause, and how much confidence you should have in them.

Here are the key columns to focus on there.

1. Coefficients: Determining the Effect Size - and Direction

The coefficient is an approximate numerical value for each independent variable describing its relationship to the dependent variable. Think of it as a simplified answer to the question, “If I change the independent variable by one unit, how much will my dependent variable be affected?” The answer is the coefficient, in mathematical terms:

A positive coefficient indicates a positive relationship: when an independent variable increases (e.g., more Facebook Ads Spent), your dependent variable also increases (Monthly Sales).

A negative coefficient indicates a negative relationship: as the independent variable goes up, the dependent variable goes down.

Let's suppose your coefficient for Facebook Ads Spending is 8.44. This means that you can expect sales to rise by $8.44, on average, for every additional dollar you spend on Facebook ads, assuming all other factors don't change. The intercept coefficient is your baseline: it tells you what your outcome would be if all of your independent variables are set to zero.

2. p-value: Which Factors are Actually Relevant?

While the Significance F value tells you if the overall model is significant, the p values for each variable tell you if the variable itself is statistically significant. You should use the same general rules of thumb here:

• A p-value less than 0.05 suggests that individual variable has a statistically significant impact on your sales. It's a factor that you should indeed be paying attention to.
• A p-value higher than 0.05 indicates that the variable is likely not a meaningful driver of total sales. Its coefficient may just be the result of random noise, the correlation may not really exist.

Your results will likely show which variables are significant and which ones are not. This is extremely useful because it points you toward doubling down on the factors that make a measurable difference, and perhaps reevaluating those which do not seem to make much of a difference in sales.

Section 3: Analyzing a Practical Example Result

To put it all together, let's see some hypothetical outcomes for your monthly sales analysis example:

How do we learn to tell the story behind all these numbers:

1. Check the overall model fit: The Significance F has an incredibly small value (it's written in scientific notation here, but it is actually 0.0000048). Since it is smaller than 0.05, the model is significant as a whole. The Adjusted R Squared Value is 0.902, which is excellent. This means that the variables selected explain about 90.2% of the changes observed in monthly sales.
2. Check individual variables: Now, let's go to the coefficients table to see which factors are driving the sales data.
3. Explain the impact factor: Now you can use your coefficients to interpret how much of an effect these choices have on your marketing strategy:

Your finalized analysis might be: We have a highly predictive model that describes 90% of our monthly sales variation. It shows that both Google and Facebook Ads are important drivers of sales, with Google Ad spending being remarkably efficient. The monthly email marketing does not display any statistical influence on this model output.

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Final Notes - Remember the Limits

Regression analysis is a powerful tool for uncovering the connections in your data, yet it does not test for cause and effect. Just because Facebook Ads have a positive coefficient value, it does not prove that they cause sales. It might show a relationship between the number of sales and Facebook Ad spend, but it also might be another factor you have not accounted for in your model that affects BOTH variables. Always combine this analytical insight with the context of your business when you build your models or make your decisions for your work.

Final Thoughts

In the end, interpreting Excel regression outputs does not have to be a daunting process. Just start by focusing on the overall model fit using the R Squared values and the Significance F value. Then direct your attention to individual drivers like your coefficients and your p-values to find what truly drives your outcomes and what the impact is. When you understand the story that the numbers are trying to tell you, you will be empowered using these insights to build your business, increase your revenue, and make smarter business decisions along the way to the future.

While Excel can be a great tool for one-off analysis, constantly pulling data from several platforms and refreshing it for reporting can be tedious. This is the exact reason we built Graphed. We help you by connecting all your business analytics platforms to one spot, giving you the ability to ask questions of your data through natural language conversation and build dashboards in seconds. Whether manually running regressions or crafting graphs, you can tell Graphed to generate a comparison of revenue-vs-spend for Facebook and Google, and it generates a live automated display for you that will update without your assistance at all, allowing you to focus on the important things like making big-picture decisions rather than losing hours to CSVs for detailed creation.